A Dynamic Optimization Method for Apparel Supply Chain Inventory Based on Intelligent Forecasting

Abstract

This invention relates to a dynamic optimization method for apparel supply chain inventory that integrates intelligent forecasting. The invention synchronously collects multi-source data, including structured fulfillment data, customer behavior maps, and strategic configuration data. It generates a channel value tensor through normalization and semantic fusion, and introduces gating activation and dynamic attention mechanisms to achieve adaptive weighting of multi-dimensional business indicators. The replenishment optimization objective function integrates time-varying weights and channel fairness constraints, and outputs differentiated replenishment strategies through convex programming. By monitoring channel replenishment revenue deviations in real time, it applies reverse fine-tuning and learning mechanisms to continuously optimize channel value representation and replenishment decisions. This invention combines a time-aware attention mechanism with a strategy-coupled optimization framework, achieving a sensitive response to real-time business conditions and a multi-objective collaborative balance in replenishment decisions, significantly outperforming the lag and rigidity problems of traditional fixed-rule or offline optimization models.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of supply chain inventory dynamic optimization and replenishment strategy decision, and particularly relates to a clothing supply chain inventory dynamic optimization method fusing intelligent prediction. BACKGROUND

[0002] At present, the field of clothing supply chain inventory dynamic optimization and replenishment strategy decision has realized data-driven fine management. Major brands and retail enterprises generally use integrated supply chain management systems to promote inventory replenishment decisions through historical sales data, real-time inventory status and basic demand prediction models. These mainstream solutions can be broadly divided into two categories: one is based on demand prediction models, such as time series analysis, regression modeling and statistical learning, combined with static cost functions such as inventory cost, stockout loss and order processing, to allocate replenishment resources uniformly; the other uses a multi-channel collaborative optimization framework to set fixed replenishment priorities or quota upper limits for different sales channels according to historical sales and inventory turnover efficiency, to achieve simple hierarchical replenishment management.

[0003] In recent years, with the development of omnichannel retailing, retail enterprises have simultaneously expanded operations across multiple online and offline channels, and the channel structure has become increasingly complex. The market positioning and profitability of products on different channels differ significantly. Existing replenishment strategies mainly use static or semi-static indicators such as historical sales and current inventory levels in their design. Some methods introduce multi-objective optimization, Markov decision, demand prediction neural networks and other algorithms to improve prediction accuracy, but still mostly use static rules such as pre-set hierarchical weights, empirical quotas or threshold controls for resource allocation between channels, lacking quantitative modeling of channel priority dynamics, high-level semantics or comprehensive business value. In practical applications, this leads to the possibility that high-value and high-potential channels may not receive timely and sufficient replenishment, while low-value or marginal channels may still occupy a share of replenishment due to historical fixed weights, resulting in overall supply-demand mismatch and resource waste.

[0004] Typical representative technologies such as replenishment strategy optimization based on sales prediction, hierarchical quota allocation, static weighting of channel KPIs, and channel-independent replenishment mechanisms, while playing a certain role in improving local channel operational efficiency, generally have the following application scope and limitations: they can only adapt to scenarios with simple channel structures and high stability of indicators, and are difficult to adjust flexibly to multiple business indicators, complex strategic objectives and dynamic market environments; they lack comprehensive characterization of high-level semantic features such as channel strategic position, customer long-term value and service fulfillment, limiting resource input efficiency and overall supply chain revenue improvement.

[0005] Through research on industry public data, it is found that the limitations of most clothing supply chain inventory management and replenishment allocation technologies mainly lie in the following aspects: There is a lack of differentiated modeling for channel priorities. Existing replenishment strategies typically only set preset weights or use static tiered classifications, which fail to reflect channel strategic positioning, brand development needs, or fully integrate heterogeneous factors such as customer lifetime value. This leads to an underestimation or misjudgment of the actual business value between channels.

[0006] Insufficient real-time and dynamic response. Fulfillment quality, repurchase rate, and market potential across different channels change constantly, but current mainstream replenishment algorithms lack the ability to integrate with corporate strategic configurations, promotional rhythms, and market changes. They cannot dynamically generate replenishment weight vectors that are tailored to different times and places, resulting in rigid decision-making and a high risk of inventory backlog or stockout losses.

[0007] The ability to integrate multiple indicators is weak. Market competition requires comprehensive consideration of multi-dimensional data such as fulfillment rate, return rate, average order value, customer behavior value, and channel KPIs. However, existing technologies mostly use single indicator-driven or simple linear weighting, which cannot capture the deep interaction and non-linear impact between multiple indicators, thus affecting the optimal allocation of replenishment resources.

[0008] The lack of feedback loops and self-evolution mechanisms is a significant drawback. Most methods lack real-time monitoring and analysis of the deviation between replenishment performance and expected goals. They fail to establish a continuous self-adjustment and semantic evolution feedback loop for strategy optimization, resulting in a long-term disconnect between replenishment priorities and actual channel performance. This limits revenue improvement and risk management capabilities.

[0009] From an industry development perspective, with the advancement of omnichannel retail, intelligent supply chains, and diversified corporate strategies, replenishment decisions based on channel value orientation, dynamic semantic modeling, and interpretable optimization will become key to improving the overall efficiency of the supply network. However, currently, there is no technology capable of elevating channel priority from static experience-based inference to a learnable and evolvable semantic tensor representation, enabling measurable, multi-dimensional heterogeneous indicator fusion, and continuously evolving intelligent allocation of replenishment weights. Summary of the Invention

[0010] This invention provides a method for dynamic optimization of apparel supply chain inventory that integrates intelligent forecasting, aiming to solve the problems of the existing technology mentioned in the background section.

[0011] The technical solution of this invention is: a dynamic optimization method for apparel supply chain inventory that integrates intelligent forecasting, comprising the following steps: S1: Collect structured fulfillment data, customer behavior mapping data, and strategic configuration data from the omnichannel retail environment; S2: Perform normalization and dimension alignment operations on the collected multi-source heterogeneous data; S3: Construct a channel value semantic tensor using a learnable weight matrix and a gated activation function; S4: Input the historical channel value semantic tensor sequence into the time-aware sliding window aggregator, combine it with the context feature vector encoded by the current inventory tightness and promotion rhythm, and generate a time-varying replenishment weight vector through a dynamic attention weight allocation mechanism; S5: The time-varying replenishment weight vector is embedded as a constraint term into the inventory optimization objective function, the original replenishment cost minimization objective is reconstructed into a weighted cost aggregation form, and a channel fairness regularization term is introduced to suppress extreme weight differentiation, thereby generating a differentiated replenishment decision optimization objective function; S6: Based on the reconstructed differentiated replenishment decision optimization objective function, solve the optimal replenishment quantity allocation scheme for each channel, execute the dynamic allocation operation of cross-channel inventory resources, and ensure that high-weight channels receive priority replenishment under the same cost conditions; S7: Monitor the deviation between actual and predicted stockout losses after replenishment is executed in each channel, and generate channel-level revenue deviation signals; S8: Based on the channel-level revenue deviation signal, the construction parameters of the channel value semantic tensor and the dynamic attention weight allocation mechanism are finely adjusted in reverse to update the semantic tensor generation rules to achieve the continuous evolution of channel priority representation.

[0012] Preferably, in step S2, the structured performance data is converted into values ​​in the [0,1] range by minimum-maximum scaling, the customer behavior graph data is linearly normalized based on the calculation results of RFM and channel attribution model, and the strategic configuration data is mapped into a fixed-dimensional vector through word embedding technology.

[0013] As a preferred embodiment, in step S2, the cross-channel behavior sequence connected by user ID is subjected to Recency time decay calculation, Frequency frequency weighted aggregation and Monetary amount normalization processing, and combined with the channel behavior attribution weight allocation algorithm to generate channel-level customer lifetime value contribution. The annual channel KPI weights, new product launch channel identifiers, and regional market penetration targets in the strategic configuration data are processed by word embedding mapping. The discretized strategic positioning labels are converted into fixed-dimensional continuous vectors using a pre-trained semantic vector space, generating a strategic positioning feature tensor that retains the semantic relationship of channel strategy.

[0014] The beneficial technical effects of this invention are as follows: 1. This invention effectively overcomes the technical defects of traditional replenishment strategies, such as overly static, coarse-grained channel priority settings and lack of differentiated modeling capabilities, by constructing a channel value semantic encoder and generating a multi-source fused channel semantic tensor. 2. This invention maps structured performance data, customer behavior graphs and strategic configuration information into computable semantic vectors, and introduces a learnable gating fusion mechanism to generate semantic tensors of high-order interactive representations. While preserving the original semantic structure of each dimension, it achieves dynamic weighted integration of cross-source information, which significantly improves the precision and expressive power of channel value characterization. 3. This invention combines a time-aware attention mechanism with a strategy-coupled optimization framework to achieve a sensitive response to real-time business conditions and a multi-objective collaborative balance in replenishment decisions, which is significantly better than the lag and rigidity problems in traditional fixed rules or offline optimization modes. 4. This invention uses the current context features as the query vector, performs sliding window attention aggregation on the historical semantic tensor sequence, and outputs a channel weight vector that evolves over time, ensuring that channels with high strategic matching degree or high performance stability are given priority protection at key nodes. 5. By embedding dynamic weights into the inventory cost objective function and introducing a channel fairness regularization term, this invention not only achieves the tilting allocation of resources towards high-value channels, but also effectively suppresses the risk of channel coverage imbalance caused by extreme weight differentiation, thus balancing efficiency and fairness. 6. This invention fundamentally solves the problem of model degradation and poor adaptability caused by the lack of feedback learning in traditional systems. The actual revenue deviation after each replenishment is used as a reinforcement signal to fine-tune the semantic encoder parameters and attention weights in reverse, so that the channel semantic representation can be continuously iterated and optimized according to the latest business performance, forming an intelligent decision-making ecosystem with self-evolution capabilities. Attached Figure Description

[0015] Figure 1 A flowchart illustrating the method of this invention; Figure 2 A schematic diagram illustrating the process of constructing the channel value semantic tensor in this embodiment of the invention; Figure 3 This is a schematic diagram of the process for generating a variable replenishment weight vector in an embodiment of the present invention. Detailed Implementation

[0016] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0017] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0018] like Figure 1 As shown in the figure, this embodiment provides a method for dynamic optimization of apparel supply chain inventory by integrating intelligent forecasting, which specifically includes the following steps: S1: Synchronously collect structured fulfillment data, customer behavior graph data, and strategic configuration data in the omnichannel retail environment. The structured fulfillment data includes the order fulfillment timeliness achievement rate, return and exchange rate, average order value, and repurchase cycle of each channel. The customer behavior graph data includes cross-channel behavior sequences based on user IDs. The strategic configuration data covers annual channel KPI weights, new product launch channel identification, and regional market penetration targets. S2: Perform normalization and dimension alignment operations on the collected multi-source heterogeneous data to eliminate the differences in dimensions between channels and generate a standardized dataset. The performance quality index is converted into a value in the range of [0,1] by minimum-maximum scaling. The customer lifetime value contribution is linearly normalized based on the calculation results of the RFM+ channel attribution model. The strategic positioning label is mapped to a fixed-dimensional vector through word embedding technology. S3: Based on normalized performance quality indicators, customer lifetime value contribution and strategic positioning labels, a channel value semantic tensor is constructed using a learnable weight matrix and a gated activation function. The gated activation function dynamically adjusts the fusion coefficient according to the interaction relationship between multiple source indicators to generate a unified semantic tensor representation that retains the correlation of multi-dimensional indicators. S4: Input the historical channel value semantic tensor sequence into the time-aware sliding window aggregator, combine it with the context feature vector encoded by the current inventory tightness and promotion rhythm, and generate a time-varying replenishment weight vector through a dynamic attention weight allocation mechanism. The dynamic attention weight allocation mechanism adjusts the contribution ratio of the historical semantic tensor of each channel according to the real-time business situation. S5: The time-varying replenishment weight vector is embedded as a constraint term into the inventory optimization objective function, the original replenishment cost minimization objective is reconstructed into a weighted cost aggregation form, and a channel fairness regularization term is introduced to suppress extreme weight differentiation, thereby generating a differentiated replenishment decision optimization objective function; S6: Based on the reconstructed differentiated replenishment decision optimization objective function, solve the optimal replenishment quantity allocation scheme for each channel, execute the dynamic allocation operation of cross-channel inventory resources, and ensure that high-weight channels receive priority replenishment under the same cost conditions; S7: Monitor the deviation between the actual stockout loss and the predicted loss after replenishment is executed in each channel, and generate a channel-level revenue deviation signal. The revenue deviation signal represents the degree of deviation between the execution effect of the replenishment strategy in the actual business scenario and the expected target. S8: Based on the channel-level revenue deviation signal, the construction parameters of the channel value semantic tensor and the dynamic attention weight allocation mechanism are finely adjusted in reverse to update the semantic tensor generation rules to achieve the continuous evolution of channel priority representation.

[0019] In this embodiment, step S1 involves the simultaneous collection of structured fulfillment data, customer behavior graph data, and strategic configuration data within the omnichannel retail environment. The structured fulfillment data includes order fulfillment timeliness achievement rate, return and exchange rate, average order value, and repurchase cycle for each channel. The customer behavior graph data contains cross-channel behavior sequences based on user IDs. The strategic configuration data covers annual channel KPI weights, new product launch channel identifiers, and regional market penetration targets, and includes the following steps: S1.1: Obtain the list of channel identifiers in the omnichannel retail environment, perform standardized channel code extraction by querying the channel management database to determine the channel scope boundary of the data to be collected, and output the standardized channel identifier list; S1.2: Based on a standardized list of channel identifiers, structured fulfillment data is collected synchronously. By calling the channel API interface, the system retrieves indicators such as order fulfillment timeliness rate, return rate, average order value and repurchase cycle to generate a structured fulfillment dataset. S1.3: Based on a standardized channel identifier list, customer behavior graph data is collected synchronously. Cross-channel behavior sequence acquisition is performed through the user ID mapping interface, including the integration of data from the entire chain of browsing, adding to cart, placing orders, fulfillment, and evaluation, in order to generate a customer behavior graph dataset. S1.4: Based on a standardized channel identifier list, strategic configuration data is collected synchronously. By reading parameters from the enterprise's strategic configuration system, such as annual channel KPI weights, new product launch channel identifiers, and regional market penetration targets, a strategic configuration dataset is generated. S1.5: Based on the structured performance dataset, customer behavior graph dataset, and strategic configuration dataset, perform multi-source data synchronization verification, and use the timestamp alignment mechanism to verify data integrity and calibrate time dimension consistency in order to generate a unified collection dataset.

[0020] In this embodiment, step S2 involves performing normalization and dimension alignment on the collected multi-source heterogeneous data to eliminate dimensional differences between channels and generate a standardized dataset. The structured performance data is converted to values ​​in the [0,1] range using minimum-maximum scaling. The customer behavior graph data is linearly normalized based on the results of RFM and channel attribution models. The strategic configuration data is mapped to a fixed-dimensional vector using word embedding technology. The steps include: S2.1: Based on customer behavior graph data, perform RFM and channel attribution model construction, calculate Recency time decay, frequency frequency weighted aggregation and Monetary amount normalization for cross-channel behavior sequences connected by user ID, and generate channel-level customer lifetime value contribution by combining channel behavior attribution weight allocation algorithm to quantify the differentiated contribution of each channel to customer value. Based on customer behavior graph data from a unified dataset, the RFM model is used to calculate the recency decay of cross-channel user behavior sequences. By defining a decay function, the time interval between the most recent transaction and the observation point is converted into a decay factor to characterize the characteristic of user activity decreasing over time, and a recency vector within the channel is generated.

[0021] Furthermore, a frequency-weighted aggregation algorithm is used to weight and statistically analyze the number of user actions triggered within the observation window. Weights are assigned based on the business importance of event types such as browsing, adding to cart, placing orders, fulfillment, and reviews. The weighted results are then aggregated into a channel-level frequency metric to reflect the intensity of user interaction with the channel.

[0022] Furthermore, the Monetary amount normalization method is used to achieve a unified scale for the cumulative transaction amount generated by users within the channel. The min-max scaling function is used to map the original amount to the [0,1] interval, eliminating the dimensional differences in single transaction amount and total amount across different channels, and forming a comparable Monetary indicator vector.

[0023] Furthermore, a channel behavior attribution weighting algorithm is used to perform channel attribution calculations on the aforementioned three types of indicators: Recency, Frequency, and Monetary. Based on the contribution of each channel in the user path during the conversion process, the indicator values ​​are allocated to the corresponding channels according to their contribution coefficients, generating a channel-level indicator matrix to ensure that cross-channel value quantification has differentiated representation capabilities.

[0024] S2.2: The minimum-maximum scaling algorithm is applied to the order fulfillment timeliness rate, return rate, average order value and repurchase cycle in the structured fulfillment data. Based on the extreme value range of historical data of each channel, the original indicators are linearly transformed to the zero-one interval values ​​to generate a normalized fulfillment quality indicator vector that eliminates the difference in dimensions. S2.3: Perform linear normalization on the channel-level customer lifetime value contribution generated in step S2.1, map the values ​​to a uniform scale range through maximum and minimum value boundary constraints, and output a standardized customer lifetime value contribution feature matrix to eliminate cross-channel value distribution offset. S2.4: Perform word embedding mapping processing on the annual channel KPI weights, new product launch channel identifiers and regional market penetration targets in the strategic configuration data. Use the pre-trained semantic vector space to convert the discretized strategic positioning labels into fixed-dimensional continuous vectors to generate a strategic positioning feature tensor that retains the channel strategic semantic relationship. For the annual channel KPI weights, new product launch channel identifiers, and regional market penetration targets in the unified data collection dataset, a word embedding mapping method is used to achieve vectorized encoding processing of discrete strategic positioning labels.

[0025] Furthermore, a distributed word vector training method is used to model the contextual relevance of strategic tags in the semantic space and obtain an initial strategic positioning vector set.

[0026] Furthermore, a vector normalization method is used to ensure that the lengths of each strategic positioning vector are consistent, and a set of unit vectors with a length of 1 is generated to eliminate the scale effect.

[0027] Furthermore, feature enhancement methods are used to achieve a joint expression of temporal and categorical features of the strategic positioning vector in the same-dimensional space, and an enhanced strategic positioning vector matrix is ​​generated.

[0028] S2.5: Perform dimension alignment and fusion operations on the normalized performance quality indicator vector generated in step S2.2, the standardized customer lifetime value contribution feature matrix output in step S2.3, and the strategic positioning feature tensor generated in step S2.4. Generate a standardized dataset of multi-source indicators through feature splicing and variance normalization to ensure that the data from each channel are computable in a unified feature space.

[0029] In this embodiment, in step S3, based on the normalized performance quality indicators, customer lifetime value contribution, and strategic positioning labels, a channel value semantic tensor is constructed using a learnable weight matrix and a gating activation function. The gating activation function dynamically adjusts the fusion coefficients according to the interaction relationships between multi-source indicators, generating a unified semantic tensor representation that preserves the correlation between multi-dimensional indicators. Figure 2 As shown, the specific steps include the following: S3.1: Based on the semantic feature dimension parameters of channel business, initialize the learnable weight matrix and global bias term to generate a learnable weight parameter set; among which, the learnable weight matrix includes the performance dimension weight matrix, the value dimension weight matrix and the positioning dimension weight matrix, and the global bias term is used to adjust the benchmark offset of multi-source indicator fusion to ensure that the subsequent gating fusion mechanism has parameter adjustability; Furthermore, the performance dimension weight matrix is ​​generated using a random uniform distribution initialization method. The value dimension weight matrix is ​​initialized using a zero-mean Gaussian distribution. This is to improve the numerical stability of the parameters in the initial iteration phase.

[0030] Furthermore, using the mean and variance of the pre-trained semantic vector space as hyperparameters, the Xavier initialization method is executed to adjust the localization dimension weight matrix. By assigning values, the gradient balancing property of the semantic mapping is realized, reducing the risk of gradient vanishing.

[0031] Furthermore, construct a global bias term vector. A constant initialization method is adopted as the benchmark offset for multi-source index fusion to ensure that the gating fusion mechanism has adjustability and stability under different initialization states.

[0032] S3.2: Using the initialized set of learnable weight parameters, a gating fusion mechanism is defined. The sigmoid activation function is used to perform nonlinear mapping processing on the linear combination result to generate a gating signal generator. The gating signal generator dynamically outputs the fusion coefficient adjustment signal according to the interaction relationship of multi-source indicators to realize the nonlinear control function of channel priority representation.

[0033] Furthermore, a nonlinear normalization process is implemented on the above linear combination results through a nonlinear mapping method, and the core response vector of the gated signal generator is obtained.

[0034] Furthermore, an indicator interaction relationship modeling method is adopted to realize the dynamic adjustment mechanism of the fusion coefficient and generate the fusion coefficient adjustment signal.

[0035] Furthermore, an adaptive control of the semantic representation of channel priority is achieved by using a weight redistribution method driven by the fusion coefficient adjustment signal, and a set of adjustment coefficients that can change over time is generated.

[0036] S3.3: Apply a learnable set of weighted parameters to the normalized channel performance semantic vector, channel value semantic vector, and channel positioning semantic vector to perform a weighted summation operation to calculate a weighted sum vector; this weighted sum vector integrates the linear combination result of multi-source semantic vectors and serves as the input data source for the gating fusion mechanism to ensure that the correlation of multi-dimensional indicators is initially fused at the numerical level. Furthermore, the weighted mapping of vectors of corresponding dimensions is achieved through matrix multiplication, resulting in a multi-dimensional weighted matrix.

[0037] Furthermore, a vector accumulation operation method is used to achieve the numerical accumulation and synthesis of multi-source semantic vectors, and to generate a preliminary fused weighted sum vector.

[0038] Furthermore, the bias term absorption algorithm is used to introduce a reference offset into the weighted sum vector, thereby improving the stability and robustness of the fusion result at the gating mechanism input stage.

[0039] Furthermore, the weighted sum vector after bias stacking is adjusted to a uniform scale range by using a numerical normalization method to ensure the scale consistency of the input data of the gating fusion mechanism.

[0040] S3.4: Input the weighted sum vector into the gated signal generator, and perform dynamic coefficient generation processing through the sigmoid activation function to generate a dynamic fusion coefficient vector; the dynamic fusion coefficient vector adjusts the contribution ratio of each dimension indicator according to the real-time business situation, realizes the adaptive mapping of the interaction relationship between multi-source indicators, and provides a non-linear weight allocation basis for semantic tensor construction; S3.5: Based on the dynamic fusion coefficient vector, the channel fulfillment semantic vector, channel value semantic vector, and channel positioning semantic vector are subjected to weighted fusion processing to generate a channel value semantic tensor. This channel value semantic tensor retains a unified representation of the correlation between multi-dimensional indicators and serves as the core output of the evolvable representation of channel priority, supporting the reconstruction of the differentiated optimization objective function for subsequent replenishment decisions.

[0041] In this embodiment, in step S4, the historical channel value semantic tensor sequence is input into a time-aware sliding window aggregator. Combined with the contextual feature vector encoded by the current inventory tightness and promotional rhythm, a time-varying replenishment weight vector is generated through a dynamic attention weight allocation mechanism. This dynamic attention weight allocation mechanism adjusts the contribution ratio of each channel's historical semantic tensor based on the real-time business situation. Figure 3 As shown, the specific steps include the following: S4.1: Based on the inventory tightness index output by the real-time inventory monitoring system and the promotion rhythm data provided by the marketing plan system, a normalization and nonlinear transformation process is performed by the feature encoder to generate a standardized context feature vector, which serves as the query input vector for the dynamic attention weight allocation mechanism. S4.2: Based on the sales cycle characteristics of the apparel industry and the system data sampling interval parameters, configure the window size parameters and time decay coefficient of the time-aware sliding window aggregator to define the aggregation time window and time-series weight distribution function of the historical channel value semantic tensor sequence; S4.3: Based on the configured time-aware sliding window aggregator parameters and standardized context feature vector, initialize the query-key matching unit and normalization unit of the dynamic attention weight allocation mechanism, set the attention score calculation function and Softmax normalization layer, so as to realize the dynamic weight generation logic based on the real-time business situation; S4.4: Input the historical channel value semantic tensor sequence into the configured time-aware sliding window aggregator, perform a sliding window aggregation operation based on the time decay coefficient, generate a historical aggregation semantic representation vector to capture the temporal dependency features of the historical semantic tensor and output the aggregation result; Based on the time-aware sliding window aggregator parameters configured in the previous steps and the loaded historical channel value semantic tensor sequence, a time decay weighted aggregation method is adopted to realize the weighted accumulation operation of semantic tensors at multiple time points within the window to highlight the contribution of recent data.

[0042] Furthermore, by using a window sliding mechanism to perform stepwise iterations on the historical semantic tensor time series, the semantic tensor quantum set that satisfies the time coverage condition in each iteration is input into the time weighting function, thereby realizing the dynamic adjustment of the window position as time progresses, and obtaining the local aggregation result matrix that evolves over time.

[0043] Furthermore, a normalization operator is used to process the above local aggregation result matrix, normalize the time weights within each window so that the weights sum to 1, ensure the consistency of the aggregation results in different time periods on the numerical scale, and generate a set of normalized aggregation weight vectors.

[0044] Furthermore, by performing element-wise weighted summation of the normalized aggregated weight vector set and the corresponding semantic tensor set elements through matrix multiplication, the historical aggregated semantic representation vector is constructed. The aggregation result retains the linear combination relationship of tensor components at each time step to reflect the temporal dependency characteristics.

[0045] Furthermore, the historical aggregate semantic representation vector is reduced in dimension by using a temporal feature compression algorithm to extract the main temporal change components and filter out low variance noise components, while retaining the principal component vector that can effectively characterize the historical dynamic features of channel priority, so as to form the final historical aggregate semantic representation output.

[0046] In the scenario of multi-channel inventory optimization in the apparel supply chain, the sliding window size W is set to 5 sampling periods, the time decay coefficient γ is set to 0.8, and the window step size Δt is set to 1 sampling period. Time decay weighted aggregation is performed on the historical channel value semantic tensor sequence of length 20, and the time weight calculation formula is adopted: ; in, For the time elapsed since the current time The Time weight of each sampling period This is the time decay coefficient.

[0047] S4.5: Input the historical aggregated semantic representation vector and the standardized context feature vector into the initialized dynamic attention weight allocation mechanism, calculate the query-key similarity and apply Softmax normalization to generate a time-varying replenishment weight vector, so as to quantify the replenishment priority of each channel under the current business situation and output the final decision basis.

[0048] In this embodiment, in step S5, the time-varying replenishment weight vector is embedded as a constraint term into the inventory optimization objective function, the original replenishment cost minimization objective is reconstructed into a weighted cost aggregation form, and a channel fairness regularization term is introduced to suppress extreme weight differentiation, generating a differentiated replenishment decision optimization objective function, specifically including the following steps: S5.1: Based on the time-varying replenishment weight vector output from the previous steps and the original inventory optimization objective function, obtain the inventory holding cost parameters, stockout penalty cost parameters, and order processing cost parameters for each channel. Use the min-max scaling algorithm to normalize the cost parameters to eliminate dimensional differences and generate a standardized set of cost parameters as the input basis for reconstructing the objective function.

[0049] S5.2: Perform a weighted aggregation operation on the standardized cost parameter set and the time-varying replenishment weight vector. Based on the dynamic weight allocation mechanism, embed the time-varying replenishment weight vector as a multiplicative constraint factor into the original inventory optimization objective function, and reconstruct the original replenishment cost minimization objective into a weighted cost aggregation form to generate a channel-differentiated cost response function, ensuring that high-weight channels receive priority replenishment under the same cost conditions. S5.3: Based on the inter-channel difference characteristics of the time-varying replenishment weight vector, the Euclidean distance calculation algorithm is used to perform difference measurement processing on the replenishment weight vector of each channel to generate a channel weight difference matrix. Then, a channel fairness regularization term is constructed through the sum of squares accumulation operation to suppress extreme differentiation of replenishment weight and maintain the stability of resource allocation between channels. Furthermore, by using the Euclidean distance calculation algorithm, any two channels are... and The weighted difference is expressed by the following formula: ; in, and Channels and channels Time-varying replenishment weight.

[0050] Furthermore, all channel combinations are traversed through a double loop. This function enables batch calculation of Euclidean distances across all channels and stores the results in a channel weight difference matrix M, where the matrix elements... correspond The value.

[0051] Furthermore, using the sum-of-squares method, all elements of the channel weight difference matrix M are squared sequentially and then summed to form the original value of the channel fairness regularization term. The calculation formula is as follows: ; in, This is the original value of the channel fairness regularization term.

[0052] Furthermore, by multiplying the regularization coefficient λ, the original value is... The mapping is a regularization term with controllable constraint strength, which realizes the function of suppressing the combination of highly differentiated weights, thereby maintaining the stability of inventory resource allocation and preventing extreme skewness.

[0053] S5.4: The channel fairness regularization term is embedded as an adjustable constraint term into the weighted cost aggregation form. Based on the Lagrange multiplier method, convex optimization processing is performed on the reconstructed objective function to integrate the channel fairness regularization term and the weighted cost aggregation form, thereby generating an intermediate optimization objective function with fairness constraints to ensure that channel replenishment decisions take into account both efficiency and fairness. S5.5: Standardize the intermediate optimization objective function with fairness constraints, use a convex programming solver to search for the global optimal solution of the objective function to generate a differentiated replenishment decision optimization objective function, and output the optimal replenishment quantity allocation scheme parameters for each channel to provide a decision basis for subsequent dynamic allocation of cross-channel inventory resources; Furthermore, by constructing the initialization process of the convex programming solver, the search space of feasible solutions in the global convex domain is set up to optimize the solver, and a solver instance object adapted to the inventory differentiation replenishment strategy constraint is obtained.

[0054] Furthermore, by calling the global search routine of the convex programming solver, the global optimal solution of the objective function under constraints is iteratively calculated, and an optimal solution vector set is generated, where each vector element corresponds to the replenishment quantity optimization variable for a single channel.

[0055] Furthermore, mathematical error analysis is used to verify the numerical stability of the solution results and generate error evaluation indicators to ensure that the global optimal solution has stability and feasibility at the business execution level.

[0056] Furthermore, the optimal solution value output by the solver is transformed into replenishment quantity allocation scheme parameters for each channel through the result mapping function, thereby realizing the final output of the differentiated replenishment decision optimization objective function.

[0057] In an omnichannel retail apparel supply chain scenario, the input parameters are set as follows: inventory holding cost coefficients for five channels are 8, 10, 7, 9, and 6; stockout penalty cost coefficients are 12, 15, 13, 14, and 11; order processing cost coefficients are 5, 6, 5, 5, and 4; time-varying replenishment weights are 0.25, 0.20, 0.30, 0.15, and 0.10; and the channel fairness regularization coefficient is set to 0.05. A normalization mapping method is used to map the above cost parameters to the [0,1] interval, and an objective function is constructed. :

[0058] in, For time-varying replenishment weight, This is the inventory holding cost coefficient. To maintain inventory levels, This is the cost coefficient for stockout penalties. For out-of-stock quantity, This is the order processing cost coefficient. For order processing volume. The iteration accuracy threshold is set using a convex programming solver. The convergence criterion is that the gradient norm is less than 1 / 3. The dynamic adjustment coefficient for the step size is calculated to be 0.85. A global optimal solution search yields optimal replenishment quantity allocation parameters of 210, 180, 240, 150, and 120 unit quantities. Error analysis shows the accuracy residual of the optimal solution is... The gradient norm is The system was tested to verify its stability and feasibility in business scenarios. Finally, the parameters of the solution were input into the cross-channel inventory allocation module to significantly improve the inventory hit rate and fulfillment efficiency of high-weight channels.

[0059] In this embodiment, step S6 involves optimizing the objective function of the reconstructed differentiated replenishment decision to solve for the optimal replenishment allocation scheme for each channel, and performing dynamic allocation of cross-channel inventory resources to ensure that high-weight channels receive priority replenishment under the same cost conditions. Specifically, this includes the following steps: S6.1: Perform parameter analysis on the objective function of the reconstructed differentiated replenishment decision optimization output in step S5, extract the time-varying replenishment weight vector and the coefficient of the channel fairness regularization term, and construct a nonlinear programming solution input model; S6.2: Based on nonlinear programming to solve the input model, execute the sequential quadratic programming algorithm to calculate the optimal replenishment quantity allocation scheme under the constraints of inventory tension and promotion rhythm in each channel, so as to generate a channel-level replenishment quantity decision vector; The input model for solving nonlinear programming based on S6.1 is implemented using the Sequential Quadratic Programming (SQP) algorithm to achieve local quadratic approximation and stepwise solution of the objective function for optimizing differentiated replenishment decisions.

[0060] Furthermore, the objective function Hessian matrix for a single iteration step is generated through a quadratic approximation. With gradient vector And construct a linearized constraint set to constrain inventory tension. and promotional pace constraints Explicit mappings are in inequality form to ensure that the feasible domain in each iteration satisfies the business conditions.

[0061] Furthermore, by solving the quadratic programming subproblem and combining the KKT conditions to obtain the direction vector and Lagrange multipliers, an iterative update scheme is generated.

[0062] Furthermore, the step size is determined using a line search method. It also performs variable updates to iteratively advance the replenishment volume towards the optimal direction.

[0063] Furthermore, through the iterative convergence criterion formula Verify the optimality of the current solution. When the threshold condition is met, terminate the iteration and output the optimal replenishment quantity decision vector for each channel.

[0064] S6.3: Perform business feasibility verification on the channel-level replenishment quantity decision vector, and perform boundary condition verification based on the real-time availability of cross-channel inventory resources and logistics timeliness constraints, so as to output a replenishment quantity allocation scheme that has passed the feasibility verification. S6.4: Generate a cross-channel inventory allocation instruction set based on the feasibility-verified replenishment allocation scheme, and combine warehouse location codes and transportation network topology to execute instruction priority sorting to form a channel weight-oriented dynamic allocation operation sequence of inventory resources; S6.5: Performs real-time scheduling control on the dynamic allocation sequence of inventory resources, triggering automated sorting equipment and transportation resources to work together through the warehouse management system interface, in order to complete the dynamic allocation of cross-channel inventory resources with priority replenishment for high-weight channels. In this embodiment, step S7 involves monitoring the deviation between the actual stockout loss and the predicted loss after replenishment is implemented in each channel, generating a channel-level revenue deviation signal. This revenue deviation signal characterizes the degree of deviation between the implementation effect of the replenishment strategy in the actual business scenario and the expected target. Specifically, it includes the following steps: S7.1: Collect inventory execution feedback data in real time to obtain the actual stockout loss value of each channel, which will serve as the input basis for deviation calculation.

[0065] S7.2: Based on the historical output records of the prediction model, obtain the predicted stockout loss value for each channel to provide a benchmark reference for deviation calculation.

[0066] S7.3: Perform a difference calculation between the actual stockout loss value and the predicted stockout loss value to calculate the stockout loss deviation value for each channel, so as to quantify the degree of deviation between the execution effect and the expected target; S7.4: Perform minimum-maximum normalization on the stockout loss deviation value to generate a channel-level standardized deviation index to eliminate dimensional differences and ensure the comparability of signals across different channels; S7.5: Encapsulate the channel-level standardized deviation index into a channel-level revenue deviation signal and store it in the feedback database to serve as reinforcement learning input for subsequent reverse fine-tuning of semantic tensor parameters.

[0067] In this embodiment, step S8 involves fine-tuning the construction parameters of the channel value semantic tensor and the dynamic attention weight allocation mechanism based on the channel-level revenue deviation signal, and updating the semantic tensor generation rules to achieve continuous evolution of channel priority representation. Specifically, this includes the following steps: S8.1: Perform data cleaning and standardization on the channel-level revenue deviation signal to remove abnormal fluctuation data points and generate a standardized deviation vector, which serves as the input condition for subsequent gradient calculation. S8.2: Based on the standardized deviation vector, the backpropagation algorithm is applied to calculate the gradient vector of the channel value semantic tensor to construct the parameters, so as to quantify the direction and magnitude of parameter adjustment. The gradient vector serves as the key basis for parameter optimization. S8.3: Perform stochastic gradient descent optimization using gradient vectors and preset learning rate to update the construction parameters of the channel value semantic tensor and generate an optimized construction parameter set, which serves as the basic input for adjusting the dynamic attention weight allocation mechanism. S8.4: The optimized parameter set is applied to the parameter fine-tuning operation of the dynamic attention weight allocation mechanism, the learnable weight configuration of the time-aware sliding window aggregator is reconstructed, and the updated dynamic attention weight allocation mechanism is generated. The updated dynamic attention weight allocation mechanism serves as the core carrier for the evolution of channel priority representation. S8.5: Perform channel fairness verification tests based on the updated dynamic attention weight allocation mechanism, calculate the prediction error index of channel-level revenue deviation signal, and generate a channel priority characterization evolution evaluation report to confirm the strategy optimization effect and guide subsequent iterations.

[0068] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this invention.

[0069] Unless otherwise defined, the technical or scientific terms used herein should be understood in their ordinary sense by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” “third,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising” or “including” and similar terms mean that the elements or objects preceding “comprising” or “including” encompass the elements or objects listed following “comprising” or “including” and their equivalents, and do not exclude other elements or objects. The “multiple” involved in the embodiments of this invention refers to two or more. A and / or B indicate three possibilities: A; B; and A and B.

[0070] The above description is merely an exemplary embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for dynamic optimization of apparel supply chain inventory that integrates intelligent forecasting, characterized in that: Includes the following steps: S1: Collect structured fulfillment data, customer behavior mapping data, and strategic configuration data from the omnichannel retail environment; S2: Perform normalization and dimension alignment operations on the collected multi-source heterogeneous data; S3: Construct a channel value semantic tensor using a learnable weight matrix and a gated activation function; S4: Input the historical channel value semantic tensor sequence into the time-aware sliding window aggregator, combine it with the context feature vector encoded by the current inventory tightness and promotion rhythm, and generate a time-varying replenishment weight vector through a dynamic attention weight allocation mechanism; S5: The time-varying replenishment weight vector is embedded as a constraint term into the inventory optimization objective function, the original replenishment cost minimization objective is reconstructed into a weighted cost aggregation form, and a channel fairness regularization term is introduced to suppress extreme weight differentiation, thereby generating a differentiated replenishment decision optimization objective function; S6: Based on the reconstructed differentiated replenishment decision optimization objective function, solve the optimal replenishment quantity allocation scheme for each channel, execute the dynamic allocation operation of cross-channel inventory resources, and ensure that high-weight channels receive priority replenishment under the same cost conditions; S7: Monitor the deviation between actual and predicted stockout losses after replenishment is executed in each channel, and generate channel-level revenue deviation signals; S8: Based on the channel-level revenue deviation signal, the construction parameters of the channel value semantic tensor and the dynamic attention weight allocation mechanism are finely adjusted in reverse to update the semantic tensor generation rules to achieve the continuous evolution of channel priority representation.

2. The method for dynamic optimization of apparel supply chain inventory based on integrated intelligent forecasting as described in claim 1, characterized in that, In step S1, the structured fulfillment data includes the order fulfillment timeliness rate, return and exchange rate, average order value and repurchase cycle of each channel; the customer behavior graph data includes cross-channel behavior sequences based on user ID; and the strategic configuration data covers annual channel KPI weights, new product launch channel identifiers and regional market penetration targets.

3. The method for dynamic optimization of apparel supply chain inventory based on integrated intelligent forecasting as described in claim 1, characterized in that, In step S2, the structured performance data is converted into values ​​in the [0,1] range by minimum-maximum scaling, the customer behavior graph data is linearly normalized based on the calculation results of RFM and channel attribution model, and the strategic configuration data is mapped into a fixed-dimensional vector through word embedding technology.

4. The method for dynamic optimization of apparel supply chain inventory based on integrated intelligent forecasting as described in claim 2, characterized in that, In step S2, the cross-channel behavior sequence connected by user ID is subjected to Recency time decay calculation, Frequency frequency weighted aggregation and Monetary amount normalization processing, and channel-level customer lifetime value contribution is generated by combining the channel behavior attribution weight allocation algorithm. The annual channel KPI weights, new product launch channel identifiers, and regional market penetration targets in the strategic configuration data are processed by word embedding mapping. The discretized strategic positioning labels are converted into fixed-dimensional continuous vectors using a pre-trained semantic vector space, generating a strategic positioning feature tensor that retains the semantic relationship of channel strategy.

5. The method for dynamic optimization of apparel supply chain inventory based on integrated intelligent forecasting as described in claim 1, characterized in that, Step S3 includes: S3.1: Initialize the learnable weight matrix and global bias term to generate a learnable weight parameter set; S3.2: A gated fusion mechanism is defined using a learnable set of weight parameters, and a sigmoid activation function is used to perform nonlinear mapping processing on the linear combination result to generate a gated signal generator; S3.3: The normalized channel fulfillment semantic vector, channel value semantic vector, and channel positioning semantic vector are processed by weighted summation operation through a learnable set of weight parameters to calculate the weighted sum vector; S3.4: Using a gated signal generator, the weighted sum vector is dynamically fused by performing dynamic coefficient generation processing through the sigmoid activation function; S3.5: Perform weighted fusion processing on the dynamic fusion coefficient vector, channel fulfillment semantic vector, channel value semantic vector, and channel positioning semantic vector to generate the channel value semantic tensor.

6. The method for dynamic optimization of apparel supply chain inventory based on integrated intelligent forecasting as described in claim 1, characterized in that, In step S4, the inventory tightness index and promotion rhythm data of the feature encoder are normalized and nonlinearly transformed to generate a standardized context feature vector. Based on the sales cycle characteristics of the apparel industry and the system data sampling interval parameters, configure the window size parameters and time decay coefficient of the time-aware sliding window aggregator to define the aggregation time window and time-series weight distribution function of the historical channel value semantic tensor sequence.

7. The method for dynamic optimization of apparel supply chain inventory based on integrated intelligent forecasting as described in claim 6, characterized in that, In step S4, based on the configured time-aware sliding window aggregator parameters and standardized context feature vector, the query-key matching unit and normalization unit of the dynamic attention weight allocation mechanism are initialized, and the attention score calculation function and Softmax normalization layer are set. Input the historical channel value semantic tensor sequence into the configured time-aware sliding window aggregator, perform a sliding window aggregation operation based on the time decay coefficient, and generate a historical aggregation semantic representation vector; The historical aggregate semantic representation vector and the standardized context feature vector are input into the initialized dynamic attention weight allocation mechanism to calculate query-key similarity and apply Softmax normalization to generate a time-varying replenishment weight vector.

8. The method for dynamic optimization of apparel supply chain inventory based on integrated intelligent forecasting as described in claim 1, characterized in that, In step S5, based on the time-varying replenishment weight vector and the original inventory optimization objective function, the inventory holding cost parameters, stockout penalty cost parameters and order processing cost parameters of each channel are obtained, and the cost parameters are normalized using the minimum-maximum scaling algorithm. A weighted aggregation operation is performed on the standardized cost parameter set and the time-varying replenishment weight vector. Based on the dynamic weight allocation mechanism, the time-varying replenishment weight vector is embedded as a multiplicative constraint factor into the original inventory optimization objective function. The original replenishment cost minimization objective is reconstructed into a weighted cost aggregation form to generate a channel-differentiated cost response function, ensuring that high-weight channels receive priority replenishment under the same cost conditions.

9. The method for dynamic optimization of apparel supply chain inventory based on integrated intelligent forecasting as described in claim 8, characterized in that, In step S5, based on the inter-channel difference characteristics of the time-varying replenishment weight vector, the Euclidean distance is used to perform difference measurement processing on the replenishment weight vector of each channel to generate a channel weight difference matrix, and a channel fairness regularization term is constructed through the sum of squares accumulation operation. The channel fairness regularization term is embedded as an adjustable constraint term into the weighted cost aggregation form. The reconstructed objective function is subjected to convex optimization based on the Lagrange multiplier method to integrate the channel fairness regularization term and the weighted cost aggregation form, thereby generating an intermediate optimization objective function with fairness constraints. The intermediate optimization objective function with fairness constraints is standardized, and a convex programming solver is used to search for the global optimal solution of the objective function to generate a differentiated replenishment decision optimization objective function, and output the optimal replenishment quantity allocation scheme parameters for each channel.

10. The method for dynamic optimization of apparel supply chain inventory based on integrated intelligent forecasting according to claim 1, characterized in that, In step S6, parameter analysis is performed based on the reconstructed differentiated replenishment decision optimization objective function to extract the time-varying replenishment weight vector and the coefficient of the channel fairness regularization term, so as to construct a nonlinear programming solution input model. When the sequential quadratic programming algorithm is executed to iteratively solve the optimization objective function based on the nonlinear programming solution input model, the inventory tension constraint and the promotion rhythm constraint are set as explicit boundary conditions of the optimization variables, and the replenishment volume decision of each channel satisfies the dual constraints of inventory and promotion.

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